Microbiome networks and change-point analysis reveal key community changes associated with cystic fibrosis pulmonary exacerbations
Bibliographic record
Abstract
Over 90% of cystic fibrosis (CF) patients die due to chronic lung infections leading to respiratory failure. The decline in CF lung function is greatly accelerated by intermittent and progressively severe acute pulmonary exacerbations (PEs). Despite their clinical impact, surprisingly few microbiological signals associated with PEs have been identified. Here we introduce an unsupervised, systems-oriented approach to identify key members of the microbiota. We used two CF sputum microbiome data sets that were longitudinally collected through periods spanning baseline health and PEs. Key taxa were defined based on three strategies: overall relative abundance, prevalence, and co-occurrence network interconnectedness. We measured the association between changes in the abundance of the key taxa and changes in patient clinical status over time via change-point detection, and found that taxa with the highest level of network interconnectedness tracked changes in patient health significantly better than taxa with the highest abundance or prevalence. We also cross-sectionally stratified all samples into the clinical states and identified key taxa associated with each state. We found that network interconnectedness most strongly delineated the taxa among clinical states, and that anaerobic bacteria were over-represented during PEs. Many of these anaerobes are oropharyngeal bacteria that have been previously isolated from the respiratory tract, and/or have been studied for their role in CF. The observed shift in community structure, and the association of anaerobic taxa and PEs lends further support to the growing consensus that anoxic conditions and the subsequent growth of anaerobic microbes are important predictors of PEs.
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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 | 0.004 |
| 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 | 0.001 |
| 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".