Bioinformatic discovery of a toxin family in Chryseobacterium piperi with sequence similarity to botulinum neurotoxins
Bibliographic record
Abstract
Clostridial neurotoxins (CNTs), which include botulinum neurotoxins (BoNTs) and tetanus neurotoxin (TeNT), are the most potent toxins known to science and are the causative agents of botulism and tetanus, respectively. The evolutionary origins of CNTs and their relationships to other proteins remains an intriguing question. Here we present a large-scale bioinformatic screen for putative toxin genes in all currently available genomes. We detect a total of 311 protein sequences displaying at least partial homology to BoNTs, including 161 predicted toxin sequences that have never been characterized. We focus on a novel toxin family from Chryseobacterium piperi with homology to BoNTs. We resequenced the genome of C. piperi to confirm and further analyze the genomic context of these toxins, and also examined their potential toxicity by expression of the protease domain of one C. piperi toxin in human cells. Our analysis suggests that these C. piperi sequences encode a novel family of metalloprotease toxins that are distantly related to BoNTs with similar domain architecture. These toxins target a yet unknown class of substrates, potentially reflecting divergence in substrate specificity between the metalloprotease domains of these toxins and the related metalloprotease domain of clostridial neurotoxins.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".