Evaluation of the electron transfer flavoprotein as an antibacterial target in <i>Burkholderia cenocepacia</i>
Bibliographic record
Abstract
There are hundreds of essential genes in multidrug-resistant bacterial genomes, but only a few of their products are exploited as antibacterial targets. An example is the electron transfer flavoprotein (ETF), which is required for growth and viability in Burkholderia cenocepacia. Here, we evaluated ETF as an antibiotic target for Burkholderia cepacia complex (Bcc). Depletion of the bacterial ETF during infection of Caenorhabditis elegans significantly extended survival of the nematodes, proving that ETF is essential for survival of B. cenocepacia in this host model. In spite of the arrest in respiration in ETF mutants, the inhibition of etf expression did not increase the formation of persister cells, when treated with high doses of ciprofloxacin or meropenem. To test if etf translation could be inhibited by RNA interference, antisense oligonucleotides that target the etfBA operon were synthesized. One antisense oligonucleotide was effective in inhibiting etfB translation in vitro but not in vivo, highlighting the challenge of reduced membrane permeability for the design of drugs against B. cenocepacia. This work contributes to the validation of ETF of B. cenocepacia as a target for antibacterial therapy and demonstrates the utility of a C. elegans liquid killing assay to validate gene essentiality in an in vivo infection model.
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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) | 0.000 | 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".