Development of a Tetracycline-Inducible Gene Expression System for the Study of Helicobacter pylori Pathogenesis
Bibliographic record
Abstract
Deletion mutants and animal models have been instrumental in the study of Helicobacter pylori pathogenesis. Conditional mutants, however, would enable the study of the temporal gene requirement during H. pylori colonization and chronic infection. To achieve this goal, we adapted the Escherichia coli Tn10-derived tetracycline-inducible expression system for use in H. pylori. The ureA promoter was modified by inserting one or two tet operators to generate tetracycline-responsive promoters, named uPtetO, and these promoters were then fused to the reporter gfpmut2 and inserted into different loci. The expression of the tetracycline repressor (tetR) was placed under the control of one of three promoters and inserted into the chromosome. Conditional expression of green fluorescent protein (GFP) in strains harboring tetR and uPtetO-GFP was characterized by measuring GFP activity and by immunoblotting. The two tet-responsive uPtetO promoters differ in strength, and induction of these promoters was inducer concentration and time dependent, with maximum expression achieved after induction for 8 to 16 h. Furthermore, the chromosomal location of the uPtetO-GFP construct and the nature of the promoter driving expression of tetR influenced the strength of the uPtetO promoters upon induction. Integration of uPtetO-GFP and tetR constructs at different genomic loci was stable in vivo and did not affect colonization. Finally, we demonstrate tetracycline-dependent induction of GFP expression in vivo during chronic infection. These results open new experimental avenues for dissecting H. pylori pathogenesis using animal models and for testing the roles of specific genes in colonization of, adaptation to, and persistence in the host.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".