First Attempt at Synthetic Microbial Communities Design for Rearing Gnotobiotic Black Soldier Fly Hermetia illucens (Linnaeus) Larvae
Bibliographic record
Abstract
Previous studies have suggested correlations between the microbiota of the black soldier fly and larval growth and bioconversion ability, primarily through functional inference. However, the concrete impact of the microbiota remains to be demonstrated. To address this, we assembled two synthetic microbial communities (SynComs) derived from endogenous bacteria and evaluated their effects on larval growth. SynComs were administered to axenic larvae reared on sterilised diet (gnotobiotic) or as a probiotic in non-sterile treatments. Larvae were reared on vegetable-based (pre-consumer vegetable residues) or on animal-based (chicken hatchery residues) substrates. The SynComs were administered at two concentrations (5 × 107 and 108 CFU per isolate) in the substrate prior to neonate introduction. SynComs improved the growth of axenic larvae compared to untreated controls, although not to the levels observed in conventionally reared larvae. In non-sterile conditions, the combined SynComs increased growth on vegetable-based substrate, but no effect was observed on the animal-based substrate, suggesting a substrate-dependent effect. These results highlight microbiota’s critical role in larval development and the potential of microbiome engineering in insect rearing systems. This preliminary study opens the way for optimisation of SynCom assemblies, which could be enhanced through pre-testing of individual isolates and selecting microbial combinations tailored to specific substrates.
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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) | 0.001 | 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 | 0.001 |
| 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".