Examining the competitive exclusion and pathogenic potential of <i>Pseudomonadota</i> isolated from healthy chickens
Bibliographic record
Abstract
The chicken intestine presents a complex environment for microbial survival due to high interbacterial competition, high bile salt concentrations, a low pH, and microaerophilic conditions. While most probiotics contain members of the Bacillota phylum, members of the Pseudomonadota phylum are known to be more important in competitive exclusion—which may be an important consideration in the formulation of future probiotics. Little is known about commensal Pseudomonadota in healthy chickens, or what benefits members of this phylum may offer the host; most studies on Pseudomonadota focus on aspects of opportunistic pathogenesis and dysbiosis. In this study, we use an in silico approach to evaluate the pathogenic potential, competition strategies, and potential host benefits of Pseudomonadota isolates from healthy chickens. We analyzed the draft genomes of 29 representative isolates of Pseudomonadota using Bagle4, AntiSMash, SeCreT6, KEGG mapper, and Virsorter2 to identify key interbacterial competition strategies including secondary metabolite biosynthesis, secretion systems, quorum sensing, and prophages. Our results revealed that each isolate exhibits distinct interbacterial competitive strategies, often independent of their taxonomic affiliation. Including Pseudomonadota in future poultry probiotics may be critical to improving colonization resistance in industrially raised poultry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".