Chironomus ramosus Larval Microbiome Composition Provides Evidence for the Presence of Detoxifying Enzymes
Bibliographic record
Abstract
Chironomids (Diptera; Chironomidae) are aquatic insects that are abundant in freshwater. We aimed to study the endogenous microbiota composition of Chironomus ramosus larvae that were sampled from the Mutha River and a laboratory culture in India. Furthermore, we performed a metagenomic analysis of the larval microbiome, sampled from the Mutha River. Significant differences were found between the bacterial community composition of C. ramosus larvae that were sampled from the Mutha River and the laboratory culture. A total of 54.7% of the amplicon sequence variants (ASVs) that were identified in the larvae from the Mutha River were unique, compared to only 12.9% of unique ASVs that were identified from the laboratory-reared larvae. The four most abundant phyla across all samples were: Proteobacteria, Fusobacteria, Firmicutes, and Bacteroidetes, while the nine most abundant genera were: Aeromonas, Alkanindiges, Breznakia, Cetobacterium, Chryseobacterium, Desulfovibrio, Dysgonomonas, Thiothrix, and Vibrio. Moreover, in the metagenomic analysis, we detected bacterial genes and bacterial pathways that demonstrated the ability to degrade different toxic compounds, detoxify metal, and confer resistance to antibiotics and UV radiation, amongst other functions. The results illuminate the fact that there are detoxifying enzymes in the C. ramosus larval microbiome that possibly play a role in protecting the insect in polluted environments.
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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".