Diet and ontogeny drastically alter the larval microbiome of the invertebrate model <i>Galleria mellonella</i>
Bibliographic record
Abstract
Larvae of the greater wax moth ( Galleria mellonella) are an emerging animal model to study the innate immune response and biodegradation of plastic polymers. Both of these complex biological processes are likely impacted by the plasticity of host–microbe interactions, which remains understudied in lepidopterans. Consequently, we carried out 16S rRNA sequencing to explore the effect diet (natural, artificial) has on the bacterial assemblages of G. mellonella in different tissues (gut, fat bodies, silk glands) throughout development (eggs, six instar stages, adults). The microbiome was rich in diversity, with Proteobacteria and Firmicutes being the most represented phyla. Contrary to other lepidopterans, G. mellonella appears to possess a resident microbiome dominated by Ralstonia. As larvae progress through development, the bacterial assemblages become increasingly shaped by the caterpillar's diet. In particular, a number of bacteria genera widely associated with the G. mellonella microbiome (e.g., Enterococcus and Enterbacter) were significantly enriched on an artificial diet. Overall, these results indicate that the G. mellonella microbiome is not as simplistic and homogenous as previously described. Rather, its bacterial communities are drastically affected by both diet and ontogeny, which should be taken into consideration in future studies planning to use G. mellonella as model species.
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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".