From pathogenesis to immune defense: a review of repeat-in-toxins (RTX) and host response
Bibliographic record
Abstract
Background and Aim Repeats-in-toxins (RTX) are a diverse family of virulence factors secreted by Gram-negative bacteria, playing a critical role in host–pathogen interactions. These multifunctional toxins disrupt host cell membranes, interfere with immune signaling, and contribute to bacterial survival and disease progression.Experimental Approach The host immune response to RTX toxins involves both innate and adaptive mechanisms, including cytokine production, inflammasome activation, and antibody-mediated neutralization. However, host-specific factors like age, sex, genetic predisposition, and environmental influences can modulate immune responses, potentially affecting disease severity and vaccine efficacy.Key Findings and Conclusions RTX toxins have been explored for both diagnostic and therapeutic applications. Their structural motifs serve as molecular markers for bacterial identification, and RTX-based vaccines, including subunit and DNA vaccines, show promise in preventing infections. However, antigenic variability and mechanisms of immune evasion pose significant hurdles to vaccine development. Moreover, challenges in vaccine development extend beyond antigenic variability, including aspects like effective delivery systems and appropriate adjuvants. Advances in computational modeling and epitope prediction may facilitate the design of broad-spectrum RTX vaccines. Future research should focus on optimizing immunization strategies and investigating RTX toxins as potential immunomodulators. Understanding RTX toxin–host interactions will be crucial for improving disease control and therapeutic interventions.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".