Identification and characterization of bacterial repeat-in-toxin adhesins using long-read genome analysis
Bibliographic record
Abstract
Abstract Gram-negative bacteria attach to host surfaces using ligand-binding domains (LBDs) at the distal tips of fibrillar RTX adhesins. Blocking the initial binding interaction(s) can potentially prevent colonization and subsequent biofilm formation and infection. To this end, adhesins must be identified, and it is also essential to determine the dominant adhesin type for those species that have more than one. RTX adhesins are frequently the largest proteins within each species (ranging from 1500 to 15,000 aa) and are often misannotated as incomplete/pseudogene products because their highly repetitive nature confounds genome assemblies from short-read technologies. Our bioinformatic process collates predicted proteins from long-read assemblies, which are then clustered based on the similarity of their C-terminal regions where the LBDs are typically located. RTX adhesins are identified by their length and domain structure and are modelled using AlphaFold3. An exhaustive search of multiple strains from seven species revealed a total of 35 different RTX adhesins that map to 16 different loci, with differing arrangements of LBDs that include putative carbohydrate-binding modules and von Willebrand Factor A-like domains. Notably, similar adhesins are sometimes found in multiple species, either by descent or through DNA uptake, and three species have an RTX adhesin of uncertain function because it lacks an obvious LBD. Importance Many bacteria initiate infection by reaching out with large, complex proteins called adhesins to attach themselves to a host cell. The DNA sequences of adhesins are difficult to read, due to their length and repetitiveness. This study leverages recent technological advances like “long-read sequencing” and structural modelling to identify and characterize the adhesins in seven species of harmful bacteria: Acinetobacter baumannii , Aeromonas hydrophila , Aeromonas salmonicida , Bordetella parapertussis , Legionella pneumophila , Vibrio parahaemolyticus , and Vibrio vulnificus . We identified thirty-five unique versions of adhesins and demonstrated their mix-and-match architecture. This research provides a foundation for strategies to block bacteria from binding surfaces, offering a vital alternative treatment as antibiotic resistance continues to rise.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".