Multiparameter Flow Cytometric Analysis of Antibiotic Effects on Membrane Potential, Membrane Permeability, and Bacterial Counts of <i>Staphylococcus aureus</i> and <i>Micrococcus luteus</i>
Bibliographic record
Abstract
Although flow cytometry has been used to study antibiotic effects on bacterial membrane potential (MP) and membrane permeability, flow cytometric results are not always well correlated to changes in bacterial counts. Using new, precise techniques, we simultaneously measured MP, membrane permeability, and particle counts of antibiotic-treated and untreated Staphylococcus aureus and Micrococcus luteus cells. MP was calculated from the ratio of red and green fluorescence of diethyloxacarbocyanine [DiOC(2)(3)]. A normalized permeability parameter was calculated from the ratio of far red fluorescence of the nucleic acid dye TO-PRO-3 and green DiOC(2)(3) fluorescence. Bacterial counts were calculated by the addition of polystyrene beads to the sample at a known concentration. Amoxicillin increased permeability within 45 min. At concentrations of <1 microg/ml, some organisms showed increased permeability but normal MP; this population disappeared after 4 h, while bacterial counts increased. At amoxicillin concentrations above 1 microg/ml, MP decreased irreversibly and the particle counts did not increase. Tetracycline and erythromycin caused smaller, dose- and time-dependent decreases in MP. Tetracycline concentrations of <1 microg/ml did not change permeability, while a tetracycline concentration of 4 microg/ml permeabilized 50% of the bacteria; 4 microg of erythromycin per ml permeabilized 20% of the bacteria. Streptomycin decreased MP substantially, with no effect on permeability; chloramphenicol did not change either permeability or MP. Erythromycin pretreatment of bacteria prevented streptomycin and amoxicillin effects. Flow cytometry provides a sensitive means of monitoring the dynamic cellular events that occur in bacteria exposed to antibacterial agents; however, it is probably simplistic to expect that changes in a single cellular parameter will suffice to determine the sensitivities of all species to all drugs.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".