Comparative proteomics of biofilm development in <i>Pseudoalteromonas tunicata</i> discovers a distinct family of Ca <sup>2+</sup> -dependent adhesins
Bibliographic record
Abstract
ABSTRACT The marine bacterium, Pseudoalteromonas tunicata , is a useful model for studying mechanisms of biofilm development due to its ability to colonize and form biofilms on a variety of marine and eukaryotic host-associated surfaces. However, the pathways responsible for P. tunicata biofilm formation are still incompletely understood, in part due to a lack of functional information for a large proportion of its proteome. Here, we used comparative shotgun proteomics to examine P. tunicata biofilm development throughout the planktonic phase to three stages of biofilm development at 24, 48, and 72 h. Proteomic analysis identified 232 proteins that were up-regulated during different stages of biofilm development, including many hypothetical proteins as well as proteins known to be important for P. tunicata biofilm development such as the autocidal enzyme AlpP, violacein proteins, S-layer protein SLR4, and various pili proteins. We further investigated the top identified biofilm-associated protein (Bap), a previously uncharacterized 1600 amino acid protein (EAR30327), which we designated as “BapP”. Based on AlphaFold modeling and genomic context analysis, we predicted BapP as a distinct Ca 2+ -dependent biofilm adhesin. Consistent with this prediction, a Δ bapP knockout mutant was defective in forming both pellicle and surface-associated biofilms, which was rescued by re-insertion of bapP into the genome. Similar to mechanisms of RTX adhesins, BapP-mediated biofilm formation was influenced by Ca 2+ levels, and BapP is likely exported by a type 1 secretion system. Ultimately, our work not only provides a useful proteomic dataset for studying biofilm development in an ecologically relevant organism, but it also adds to our knowledge of bacterial adhesin diversity, emphasizing Bap-like proteins as widespread determinants of biofilm formation in bacteria.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".