FimX regulates type IV pilus localization via the Pil–Chp chemosensory system in <i>Acinetobacter baylyi</i>
Bibliographic record
Abstract
Type IV pili (T4P) are widespread dynamic appendages required for diverse prokaryotic behaviors, including twitching motility, biofilm formation, and DNA uptake leading to natural transformation. Although the components involved in T4P assembly and dynamics are largely conserved across divergent clades of bacteria, the mechanisms underlying T4P function and regulation differ significantly and remain poorly characterized outside of a select few model organisms. One understudied characteristic of T4P includes the spatial organization of T4P envelope-spanning nanomachines and how organizational patterns contribute to single-cell behaviors. The bacterial species Acinetobacter baylyi localizes its T4P nanomachines in a unique pattern along the long axis of the cell, making it a robust model to study the mechanisms underlying the regulation of intracellular organization in single-cell organisms. In this work, we find that the T4P regulatory protein FimX has been functionally repurposed from regulating T4P dynamics to instead control T4P positioning through a chemosensory Pil–Chp pathway. We show that FimX directly interacts with the Pil–Chp histidine kinase ChpA that likely influences Pil–Chp signaling and the subsequent positioning of T4P machines. These data contribute to our understanding of how bacterial regulatory systems can evolve to function in diverse biological processes.
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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 | 0.001 |
| 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".