Unveiling the molecular mechanisms of the type IX secretion system's response regulator: Structural and functional insights
Bibliographic record
Abstract
Abstract The type IX secretion system (T9SS) is a nanomachinery utilized by bacterial pathogens to facilitate infection. The system is regulated by a signaling cascade serving as its activation switch. A pivotal member in this cascade, the response regulator protein PorX, represents a promising drug target to prevent the secretion of virulence factors. Here, we provide a comprehensive characterization of PorX both in vitro and in vivo. First, our structural studies revealed PorX harbors a unique enzymatic effector domain, which, surprisingly, shares structural similarities with the alkaline phosphatase superfamily, involved in nucleotide and lipid signaling pathways. Importantly, such pathways have not been associated with the T9SS until now. Enzymatic characterization of PorX's effector domain revealed a zinc-dependent phosphodiesterase activity, with active site dimensions suitable to accommodate a large substrate. Unlike typical response regulators that dimerize via their receiver domain upon phosphorylation, we found that zinc can also induce conformational changes and promote PorX's dimerization via an unexpected interface. These findings suggest that PorX can serve as a cellular zinc sensor, broadening our understanding of its regulatory mechanisms. Despite the strict conservation of PorX in T9SS-utilizing bacteria, we demonstrate that PorX is essential for virulence factors secretion in Porphyromonas gingivalis and affects metabolic enzymes secretion in the nonpathogenic Flavobacterium johnsoniae, but not for the secretion of gliding adhesins. Overall, this study advances our structural and functional understanding of PorX, highlighting its potential as a druggable target for intervention strategies aimed at disrupting the T9SS and mitigating virulence in pathogenic species.
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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 | 0.001 |
| 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".