Additional file 1 of Transplantation of bacteriophages from ulcerative colitis patients shifts the gut bacteriome and exacerbates the severity of DSS colitis
Bibliographic record
Abstract
Additional file 1: Supplementary Figure S1. Virome analyses on inoculum and mouse VLPs. Related to Figs. 2 and 5. Supplementary Figure S2. Experimental colitis severity of UC-HMA and healthy-HMA mice given a single dose of healthy or UC VLPs. Related to Fig. 3. Supplementary Figure S3. Viral and bacterial abundance in UC-HMA mice given UC or healthy VLPs. Related to Fig. 4. Supplementary Figure S4. Viral and bacterial abundance in UC-HMA mice given intact or heat-killed UC VLPs. Related to Fig. 4. Supplementary Figure S5. Bacterial activity in HMA mice given UC or healthy VLPs. Related to Fig. 4. Supplementary Figure S6. Bacterial damage in HMA mice given UC or healthy VLPs. Related to Fig. 4. Supplementary Figure S7. Richness of viral scaffolds and viral clusters in UC-HMA mice given healthy or UC VLPs. Related to Fig. 5. Supplementary Figure S8. Jaccard distance to pooled healthy and UC VLP inoculums over time in UC-HMA mice given UC or healthy VLPs. Related to Fig. 5. Supplementary Figure S9. NMDS on Bray-Curtis dissimilarity of viral clusters between UC-HMA mice given UC or healthy VLPs. Related to Fig. 5. Supplementary Figure S10. PCoA on weighted UniFrac distance between UC-HMA mice given UC or healthy VLPs. Related to Fig. 6. Supplementary Figure S11. PCoA on weighted UniFrac distance between UC-HMA mice given UC VLPs or heat-killed UC VLPs. Related to Fig. 6. Supplementary Figure S12. Relative abundance of temperate phages. Related to Fig. 6. Supplementary Figure S13. DSS does not induce UC gut bacterial prophages in vitro. Related to Fig. 6. Supplementary Figure S14. Human microbiota protects mice from experimental colitis. Related to Fig. 7. Supplementary Table S2. PERMANOVA and effect size of weighted UniFrac distances of bacterial communities between UC-HMA mice given a single dose of healthy VLPs or UC VLPs. Related to Fig. 3. Supplementary Table S3. PERMANOVA and effect size of weighted UniFrac distances of bacterial communities between healthy-HMA mice given a single dose of healthy VLPs or UC VLPs at each sampling point. Related to Fig. 3. Supplementary Table S4. PERMANOVA and effect size of weighted UniFrac distances of bacterial communities between UC-HMA mice and healthy-HMA mice at each sampling point. Related to Fig. 3. Supplementary Table S7. PERMANOVA and effect size of Bray-Curtis dissimilarity on viral scaffolds and viral clusters between UC-HMA mice given healthy and UC VLPs. Related to Fig. 5. Supplementary Table S9. Differentially abundant bacterial species during in HMA mice given healthy VLPs, UC VLPs, or PBS. Related to Fig. 6. Supplementary Table S10. Differentially abundant bacterial species in HMA mice given UC VLPs (+/- DSS), or heat-killed UC VLPs. Related to Fig. 6. Supplementary Table S11. PERMANOVA and effect size of weighted UniFrac distance on bacterial communities between UC-HMA mice given healthy VLPs, UC VLPs or PBS at each sampling point. Related to Fig. 5. Supplementary Table S12. PERMANOVA and effect size of weighted UniFrac distance on bacterial communities between UC-HMA mice given healthy VLPs, UC VLPs or PBS at each sampling point. Related to Fig. 5. Supplementary Table S13. PERMANOVA and effect size of weighted UniFrac distance on bacterial communities between UC-HMA mice given UC VLPs (+DSS), UC VLPs (-DSS) or heat-killed UC VLPs (+DSS) at each sampling point. Related to Fig. 5.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.886 | 0.194 |
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".