Sodium Ion Cycle in Bacterial Pathogens: Evidence from Cross-Genome Comparisons
Bibliographic record
Abstract
Analysis of the bacterial genome sequences shows that many human and animal pathogens encode primary membrane Na+ pumps, Na+-transporting dicarboxylate decarboxylases or Na+ translocating NADH:ubiquinone oxidoreductase, and a number of Na+ -dependent permeases. This indicates that these bacteria can utilize Na+ as a coupling ion instead of or in addition to the H+ cycle. This capability to use a Na+ cycle might be an important virulence factor for such pathogens as Vibrio cholerae, Neisseria meningitidis, Salmonella enterica serovar Typhi, and Yersinia pestis. In Treponema pallidum, Chlamydia trachomatis, and Chlamydia pneumoniae, the Na+ gradient may well be the only energy source for secondary transport. A survey of preliminary genome sequences of Porphyromonas gingivalis, Actinobacillus actinomycetemcomitans, and Treponema denticola indicates that these oral pathogens also rely on the Na+ cycle for at least part of their energy metabolism. The possible roles of the Na+ cycling in the energy metabolism and pathogenicity of these organisms are reviewed. The recent discovery of an effective natural antibiotic, korormicin, targeted against the Na+ -translocating NADH:ubiquinone oxidoreductase, suggests a potential use of Na+ pumps as drug targets and/or vaccine candidates. The antimicrobial potential of other inhibitors of the Na+ cycle, such as monensin, Li+ and Ag+ ions, and amiloride derivatives, is discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".