Tracking bacterial pathogens with genetically‐encoded reporters
Bibliographic record
Abstract
During the infectious process, bacterial pathogens are subject to changes in environmental conditions such as nutrient availability, immune response challenges, bacterial density and physical contacts with targeted host cells. These conditions occur in the colonized organs, in diverse regions within infected tissues or even at the subcellular level for intracellular pathogens. Integration of environmental cues leads to measurable biological responses in the bacterium required for adaptation. Recent progress in technology enabled the study of bacterial adaptation in situ using genetically encoded reporters that allow single cell analysis or whole body imaging based on fluorescent proteins, alternative fluorescent assays or luciferases. This review presents a historical perspective and technical details on the methods used to develop transcriptional reporters, protein-protein interaction assays and secretion detection assays to study pathogenic bacteria adaptation in situ. Finally, studies published in the last 5 years on gram positive and gram negative bacterial adaptation to the host during infection are discussed. However, the methods described here could easily be extended to study complex microbial communities within host tissue and in the environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".