Effects of municipal wastewater effluents on the digestive gland microbiome of wild freshwater mussels (Lasmigona costata)
Bibliographic record
Abstract
Gut microbial communities are vital for maintaining host health, and are sensitive to diet, environment, and chemical exposures. Wastewater treatment plants (WWTPs) release effluents containing antimicrobials, pharmaceuticals, and other contaminants that may negatively affect the gut microbiome of downstream organisms. This study investigated changes in the diversity and composition of the digestive gland microbiome of flutedshell mussels (Lasmigona costata) from upstream and downstream of two large (service >100,000) WWTPs. Mussel digestive gland microbiome was analyzed following the extraction, PCR amplification, and sequencing of bacterial DNA using the V3-V4 hypervariable regions of the 16 S rRNA gene. Bacterial alpha diversity decreased at sites downstream of the second WWTP and these sites were dissimilar in beta diversity from sites upstream and downstream of the first upstream WWTP. The microbiomes of mussels collected downstream of the first WWTP had increased relative abundances of Actinobacteria, Bacteroidetes, and Firmicutes, with a decrease in Cyanobacteria, compared to upstream mussels. Meanwhile, those collected downstream of the second WWTP increased in Proteobacteria and decreased in Actinobacteria, Bacteroidetes, and Tenericutes. Increased Proteobacteria has been linked to adverse effects in mammals, but their functions in mussels is currently unknown. Finally, effluent-derived bacteria were found in the microbiome of mussels downstream of both WWTPs but not in those from upstream. Overall, results show that the digestive gland microbiome of mussels collected upstream and downstream of WWTPs differed, which has implications for altered host health and the transport of WWTP-derived bacteria through aquatic ecosystems.
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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 | 0.001 |
| 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".