Electroporation of Functional Bacterial Effectors into Mammalian Cells
Bibliographic record
Abstract
The study of protein interactions in the context of living cells can generate critical information about localization, dynamics, and interacting partners. This information is particularly valuable in the context of host-pathogen interactions. Many pathogen proteins function within host cells in a variety of way such as, enabling evasion of the host immune system and survival within the intracellular environment. To study these pathogen-protein host-cell interactions, several approaches are commonly used, including: in vivo infection with a strain expressing a tagged or mutant protein, or introduction of pathogen genes via transfection or transduction. Each of these approaches has advantages and disadvantages. We sought a means to directly introduce exogenous proteins into cells. Electroporation is commonly used to introduce nucleic acids into cells, but has been more rarely applied to proteins although the biophysical basis is exactly the same. A standard electroporator was used to introduce affinity-tagged bacterial effectors into mammalian cells. Human epithelial and mouse macrophage cells were cultured by traditional methods, detached, and placed in 0.4 cm gap electroporation cuvettes with an exogenous bacterial pathogen protein of interest (e.g. Salmonella Typhimurium GtgE). After electroporation (0.3 kV) and a short (4 hr) recovery period, intracellular protein was verified by fluorescently labeling the protein via its affinity tag and examining spatial and temporal distribution by confocal microscopy. The electroporated protein was also shown to be functional inside the cell and capable of correct subcellular trafficking and protein-protein interaction. While the exogenous proteins tended to accumulate on the surface of the cells, the electroporated samples had large increases in intracellular effector concentration relative to incubation alone. The protocol is simple and fast enough to be done in a parallel fashion, allowing for high-throughput characterization of pathogen proteins in host cells including subcellular targeting and function of virulence proteins.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".