16S rRNA Mutations That Confer Tetracycline Resistance in<i>Helicobacter pylori</i>Decrease Drug Binding in<i>Escherichia coli</i>Ribosomes
Bibliographic record
Abstract
Tetracycline resistance in clinical isolates of Helicobacter pylori has been associated with nucleotide substitutions at positions 965 to 967 in the 16S rRNA. We constructed mutants which had different sequences at 965 to 967 in the 16S rRNA gene present on a multicopy plasmid in Escherichia coli strain TA527, in which all seven rrn genes were deleted. The MICs for tetracycline of all mutants having single, double, or triple substitutions at the 965 to 967 region that were previously found in highly resistant H. pylori isolates were higher than that of the mutant exhibiting the wild-type sequence of tetracycline-susceptible H. pylori. The MIC of the mutant with the 965TTC967 triple substitution was 32 times higher than that of the E. coli mutant with the 965AGA967 substitution present in wild-type H. pylori. The ribosomes extracted from the tetracycline-resistant E. coli 965TTC967 variant bound less tetracycline than E. coli with the wild-type H. pylori sequence at this region. The concentration of tetracycline bound to the ribosome was 40% that of the wild type. The results of this study suggest that tetracycline binding to the primary binding site (Tet-1) of the ribosome at positions 965 to 967 is influenced by its sequence patterns, which form the primary binding site for tetracycline.
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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 | 0.001 |
| 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) | 0.002 | 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".