L-norepinephrine induces community shift, oxidative stress response, metabolic reprogramming, and virulence potential in wastewater microbiomes
Bibliographic record
Abstract
Neuroendocrine compounds discharged into wastewater systems represent an emerging challenge at the intersection of human physiology and environmental microbiology. l-norepinephrine (L-NE), which has been recognized to potentiate growth of human and animal bacterial pathogens, is discharged in sewage through urine and faeces. While extensive pure culture studies have established l-NE's capacity to modulate bacterial virulence through iron acquisition and quorum sensing pathways, its impact on complex microbial communities, where intricate metabolic networks and interspecies interactions dominate, remains largely unexplored. This knowledge gap is particularly critical as urbanization drives increasing neuroendocrine compound loads in wastewater influents in metropolitan areas. Through parallel treatments of l-NE (1 × 10⁻⁵ M to 1 × 10⁻⁴ M), dextrose, and H₂O₂ in municipal and agricultural wastewater communities, we uncovered sophisticated metabolic and regulatory mechanisms that challenge the conventional understanding of microbial substrate utilization. Despite containing 10-fold less carbon, l-NE treatments achieved superior growth (10⁸ CFU mL⁻¹) while maintaining Pseudomonadaceae-dominated communities. Targeted metaproteomics revealed coordinated upregulation of oxidative stress genes (oxyR, soxRS) and antioxidant enzymes, while proteome-constrained metabolic modeling demonstrated distinct pathway modulation in central carbon and nitrogen metabolism. Notably, when compared to dextrose-supplemented controls, representing typical carbon substrate utilization, l-NE treatments showed similar taxonomic profiles without preferential enrichment of known pathogenic families. However, l-NE significantly enhanced autoinducer gene (luxS, qseC) expression, suggesting increased virulence potential through community-level metabolic reprogramming. These findings reveal l-NE as a potent modulator of microbial community dynamics in engineered ecosystems, with important implications for treatment process stability and downstream environmental impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".