Quantitative PCR-Based Competitive Index for High-Throughput Screening of <i>Salmonella</i> Virulence Factors
Bibliographic record
Abstract
Salmonella enterica serovar Typhimurium is an intracellular pathogen and a main cause of food-borne illness. In this study, a quantitative PCR (qPCR)-based competitive index (CI) method was developed to simultaneously compare the growth of multiple Salmonella strains. This method was applied to a mixture of 17 Salmonella mutants lacking regulator genes, and their survival ratios were compared based on expression of natural resistance-associated macrophage protein 1 (Nramp1). Nramp1, as a major host innate immune component, controls the intracellular replication of pathogens. Deletion strains containing unique DNA barcodes in place of regulator genes were mixed with the parental control, and the bacteria were inoculated into congenic mice differing only at Nramp1. Most of the deletion strains were outcompeted by wild-type bacteria in either mouse strain, and the lack of Nramp1 didn't increase the tested strain/parent control replication ratios. When the same collection of mutants was tested in congenic mouse-derived primary macrophages, a major Nramp1-expressing cell type, six strains (ΔhimD, ΔphoP/phoQ, ΔrpoE, ΔrpoS, ΔompR/envZ, and Δhfq strains) grew better in Nramp1(-/-) than in Nramp1(+/+) macrophages, suggesting that these six regulators may play roles in overcoming Nramp1-mediated bactericidal activity in primary macrophages. The discrepancy in survival of macrophages and that of mice suggests either that there are differences in macrophage populations or that other cell types expressing Nramp1 control Salmonella proliferation in the host. The method described allows competitive infection analysis to be carried out on complex mixtures of bacteria and provides high reproducibility from independent biological replicates.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".