Legionella pneumophila Transcriptional Response following Exposure to CuO Nanoparticles
Bibliographic record
Abstract
Copper ions are an effective antimicrobial agent used to control Legionnaires' disease and Pontiac fever arising from institutional drinking water systems. Here, we present data on an alternative bactericidal agent, copper oxide nanoparticles (CuO-NPs), and its efficacy on Legionella pneumophila. In broth cultures, the CuO-NPs caused growth inhibition, which appeared to be concentration and exposure time dependent. The transcriptomic response of L. pneumophila to CuO-NP exposure was investigated by using a whole-genome microarray. The expression of genes involved in metabolism, transcription, translation, DNA replication and repair, and unknown/hypothetical proteins was significantly affected by exposure to CuO-NPs. In addition, expression of 21 virulence genes was also affected by exposure to CuO-NP and further evaluated by quantitative reverse transcription-PCR (qRT-PCR). Some virulence gene responses occurred immediately and transiently after addition of CuO-NPs to the cells and faded rapidly (icmV, icmW, lepA), while expression of other genes increased within 6 h (ceg29, legLC8, legP, lem19, lem24, lpg1689, and rtxA), 12 h (cegC1, dotA, enhC, htpX, icmE, pvcA, and sidF), and 24 h (legP, lem19, and ceg19), but for most of the genes tested, expression was reduced after 24 h of exposure. Genes like ceg29 and rtxA appeared to be the most responsive to CuO-NP exposures and along with other genes identified in this study may prove useful to monitor and manage the impact of drinking water disinfection on L. pneumophila.
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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".