Systematic Identification of Intracellular-Translocated Candidate Effectors in Edwardsiella piscicida
Bibliographic record
Abstract
Many bacterial pathogens inject effectors directly into host cells to target a variety of host cellular processes and promote bacterial dissemination and survival. Identifying the bacterial effectors and elucidating their functions are central to understanding the molecular pathogenesis of these pathogens. Edwardsiella piscicida is a pathogen with a wide host range, and very few of its effectors have been identified to date. Here, based on the genes significantly regulated by macrophage infection, we identified 25 intracellular translocation-positive candidate effectors, including all five previously reported effectors, namely EseG, EseJ, EseH, EseK and EvpP. A subsequent secretion analysis revealed diverse secretion patterns for the 25 effector candidates, suggesting that multiple transport pathways were involved in the internalization of these candidate effectors. Further, we identified two novel type VI secretion system (T6SS) putative effectors and three outer membrane vesicles (OMV)-dependent putative effectors among the candidate effectors described above, and further analyzed their contribution to bacterial virulence in a zebrafish model. This work demonstrates an effective approach for screening bacterial effectors and expands the effectors repertoire in E. piscicida.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".