Meta-Transcriptomic Response to Copper Corrosion in Drinking Water Biofilms
Bibliographic record
Abstract
Drinking water biofilm ecosystems harbor complex and dynamic prokaryotic and eukaryotic microbial communities. However, little is known about the impact of copper corrosion on microbial community functions in metabolisms and resistance. This study was conducted to evaluate the impact of upstream Cu pipe materials on downstream viable community structures, pathogen populations, and metatranscriptomic responses of the microbial communities in drinking water biofilms. Randomly transcribed cDNA was generated and sequenced from downstream biofilm samples of either unplasticized polyvinylchloride (PVC) or Cu coupons. Diverse viable microbial organisms with enriched pathogen-like organisms and opportunistic pathogens were active in those biofilm samples. Cu-influenced tubing biofilms had a greater upregulation of genes associated with potassium (K) metabolic pathways (i.e., K-homeostasis, K-transporting ATPase, and transcriptional attenuator), and a major component of the cell wall of mycobacteria (mycolic acids) compared to tubing biofilms downstream of PVC. Other upregulated genes on Cu influenced biofilms included those associated with stress responses (various oxidative resistance genes), biofilm formation, and resistance to toxic compounds. Downregulated genes included those associated with membrane proteins responsible for ion interactions with potassium; respiration-electron-donating reactions; RNA metabolism in eukaryotes; nitrogen metabolism; virulence, disease, and defense; and antibiotic resistance genes. When combined with our previous identification of biofilm community differences, our studies reveal how microbial biofilms adapt to Cu plumbing conditions by fine-tuning gene expression, altering metabolic pathways, and optimizing their structural organization. This study offers new insights into how copper pipe materials affect the development and composition of biofilms in premise plumbing. Specifically, it highlights copper's role in inhibiting the growth of many microbes while also contributing to the resistance of some microbes within the drinking water biofilm 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".