Visualization of Vacuolar Acidification-induced Transcription of Genes of Pathogens inside Macrophages
Bibliographic record
Abstract
The objective of these studies was to analyze the role of the ionic environment of phagosomal vacuoles in the control of pathogens by macrophages. Digital imaging and flow cytometry were used to follow the induction of the phoP promoter of Salmonella enterica Typhimurium within live macrophages. Manipulating the Mg2+ concentration within the Salmonella-containing vacuole (SCV) was without effect on the early induction of PhoPQ. Moreover, direct measurement of [Mg2+] within the SCV using nanosensor particles showed that, during this initial period of phoP activation, the concentration of the divalent cation is rapidly regulated and stabilizes around 1 mm. Extrusion of other divalent cations via the Nramp1 efflux pump was similarly ruled out as an important contributor to the activation of the regulon. By contrast, induction of PhoP was greatly attenuated when the pH gradient across the SCV membrane was dissipated. A second, more modest pH-independent component of PhoP induction was unmasked by inhibition of the vacuolar proton pump. This second component was eliminated by pretreatment of cells with IFNgamma, even though the cytokine augmented the overall PhoP response. These findings demonstrate the existence of at least three separate activators of phoP transcription: resting and IFNgamma-stimulated pH-sensitive components, plus a pH-independent component.
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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) | 0.001 | 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".