Rewiring Gram-Negative Bacteria Cell Surfaces with Bio-Orthogonal Chemistry via Liposome Fusion
Bibliographic record
Abstract
The ability to tailor bacteria cell surfaces with non-native molecules is critical to advance the study of bacteria communication, cell behavior, and for next-generation therapeutics to improve livestock and human health. Such modifications would allow for novel control over cell behavior, cell-cell interactions, biofilm formation, adjuvant conjugation, and imaging. Current methods to engineer bacteria surfaces have made major advances but rely on complicated, slow, and often expensive molecular biology and metabolic manipulation methods with limited scope on the type of molecules installed onto the surface. In this report, we introduce a new straightforward method based on liposome fusion to engineer Gram-negative bacteria cells with bio-orthogonal groups that can subsequently be conjugated to a range of molecules (biomolecules, small molecules, probes, proteins, nucleic acids, ligands, and radiolabels) for further studies and programmed behavior of bacteria. This method is fast, efficient, inexpensive, and useful for installing a broad scope of ligands and biomolecules to Gram-negative bacteria surfaces.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".