Additional file 5 of Metabolic modeling of microbial communities in the chicken ceca reveals a landscape of competition and co-operation
Bibliographic record
Abstract
Supplementary Material 5. Supplementary Figure 5. Influence of species-specific metabolic potential and key taxa presence on cross-feeding activity. A-B. Correlation plots showing the relationship between the average number of unique EC numbers presented in a specie within a sample and (A) the total number of interactions formed by a specie normalized by the number of samples where a specie is present (Pearson) (B) the number of samples where the specie is present (Pearson). Red line is fitted linear regression, and gray area indicates the 95% confidence interval. Each data point represents a species. Only species with at least 10 cross-feeding pairs involving at least 12 metabolites are shown. Selected species (present in at least two samples, above the threshold of 230 total interactions per sample and average number of unique EC per sample > 5) have labels that are color-coded corresponding to the taxonomic family they belong to. C. Effect of E. coli presence across both community categories (HB - High Bacteroides; NB - no Bacteroides) on cross-feeding activity, measured by the cross-feeding coefficient (CFC). Each dot corresponds to a CFC calculated for a single sample. Points and lines are color-coded based on E. coli abundance: red represents communities with high E. coli abundance (>5%), blue represents communities with low E. coli abundance (<5%), and gray represents communities where E. coli is absent. In the NB group, higher E. coli abundance is associated with a notable increase in cross-feeding activity compared to low or absent E. coli, suggesting a significant role for E. coli in enhancing cross-feeding, particularly in the absence of Bacteroides. Conversely, in HB communities, E. coli presence appears to have a more muted effect on cross-feeding, especially in low abundance. D-E. Interaction effects of E. coli and A. butyraticus (D)or L. crispatus (E)on cross-feeding activity, measured by the CFC, across categories. Different colors represent distinct interaction scenarios: E. coli & A. butyraticus (D)/L. crispatus (E) absent (gray), E. coli absent & A. butyraticus (D)/L. crispatus (E) present (cyan), low-abundance E. coli & A. butyraticus (D)/L. crispatus (E) absent (blue), low-abundance E. coli & A. butyraticus (D)/L. crispatus (E) present (orange), high-abundance E. coli & A. butyraticus (D)/L. crispatus (E) absent (red), and high-abundance E. coli & A. butyraticus (D)/L. crispatus (E) present (purple). Each point represents an average CFC for samples within each interaction scenario. NB communities with high E. coli abundance and without A. butyraticus (solid red line, D) show the highest cross-feeding activity. When A. butyraticus is present (dotted lines, D), the interaction patterns change: the cross-feeding coefficient decreases slightly in communities with high E. coli, but it increases in communities with low or no E. coli. This suggests that A. butyraticus may partly compensate for the absence or lower abundance of E. coli, contributing to cross-feeding, particularly in NB communities. The interaction between A. butyraticus and E. coli seems to reveal a potentially competitive relationship where the presence of both may reduce cross-feeding, while the presence of either one alone promotes cross-feeding in different ways. Presence of L. crispatus is associated with increased cross-feeding activity in NB compared to HB communities (E), regardless of E. coli abundance, while absence of L. crispatus doesn’t increase significantly the cross-feeding activity in NB when E. coli levels are low. This points to an impact of antagonistic relationship between L. crispatus and Bacteroides rather than potential competitive relationship between L. crispatus and E. coli.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.812 | 0.184 |
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".