Direct measurement of oxidative and nitrosative stress dynamics in <i>Salmonella</i> inside macrophages
Bibliographic record
Abstract
Many significant bacterial pathogens have evolved virulence mechanisms to evade degradation and exposure to reactive oxygen (ROS) and reactive nitrogen species (RNS), allowing them to survive and replicate inside their hosts. Due to the highly reactive and short-lived nature of ROS and RNS, combined with limitations of conventional detection agents, the mechanisms underlying these evasion strategies remain poorly understood. In this study, we describe a system that uses redox-sensitive GFP to nondisruptively measure real-time fluctuations in the intrabacterial redox environment. Using this system coupled with high-throughput microscopy, we report the intrabacterial redox dynamics of Salmonella enterica Typhimurium (S. Typhimurium) residing inside macrophages. We found that the bacterial SPI-2 type III secretion system is required for ROS evasion strategies and this evasion relies on an intact Salmonella-containing vacuole (SCV) within which the bacteria reside during infection. Additionally, we found that cytosolic bacteria that escape the SCV experience increased redox stress in human and murine macrophages. These results highlight the existence of specialized evasion strategies used by intracellular pathogens that either reside inside a vacuole or "escape" into the cytosol. Taken together, the use of redox-sensitive GFP inside Salmonella significantly advances our understanding of ROS and RNS evasion strategies during infection. This technology can also be applied to measuring bacterial oxidative and nitrosative stress dynamics under different conditions in a wide variety of bacteria.
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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) | 0.000 | 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) | 0.000 | 0.000 |
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".