Metal-induced bacterial interactions promote diversity in river-sediment microbiomes
Bibliographic record
Abstract
Anthropogenic metal contamination results in long-term environmental selective pressure with unclear impacts on bacterial communities, which comprise key players in ecosystem functioning. Since metal contamination poses serious toxicity and bioaccumulation issues, assessing their impact on environmental microbiomes is important to respond to current environmental and health issues. Despite elevated metal concentrations, the river sedimentary microbiome near the MetalEurop foundry (France) shows unexpected higher diversity compared with the upstream control site. In this work, a follow-up of the microbial community assembly during a metal contamination event was performed in microcosms with periodic renewal of the supernatant river water. Sediments of the control site were gradually exposed to a mixture of metals (Cd, Cu, Pb and Zn) in order to reach similar concentrations to MetalEurop sediments. Illumina sequencing of 16S rRNA gene amplicons was performed. Metal-resistant genes, czcA and pbrA, as well as IncP plasmid content, were assessed by quantitative PCR. The outcomes of this study support previous in situ observations showing that metals act as community assembly managers, increasing diversity. This work revealed progressive adaptation of the sediment microbiome through the selection of different metal-resistant mechanisms and cross-species interactions involving public good-providing bacteria co-occurring with the rest of the community.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".