Network Analysis Highlights Complex Interactions between Pathogen, Host and Commensal Microbiota
Bibliographic record
Abstract
Interactions between bacteria and their host represent a full continuum from pathogenicity to mutualism. From an evolutionary perspective, host-bacteria relationships are no longer considered a two-component system but rather a complex network. In this study, we focused on the relationship between brook charr (Salvelinus fontinalis) and bacterial communities developing on skin mucus. We hypothesized that stressful conditions such as those occurring in aquaculture production induce shifts in the bacterial community of healthy fish, thus allowing pathogens to cause infections. The results showed that fish skin mucus microbiota taxonomical structure is highly specific, its diversity being partly influenced by the surrounding water bacterial community. Two types of taxonomic co-variation patterns emerged across 121 contrasted communities' samples: one encompassing four genera well known for their probiotic properties, the other harboring five genera mostly associated with pathogen species. The homeostasis of fish bacterial community was extensively disturbed by induction of physiological stress in that both: 1) the abundance of probiotic-like bacteria decreased after stress exposure; and 2) pathogenic bacteria increased following stress exposure. This study provides further insights regarding the role of mutualistic bacteria as a primary host protection barrier.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".